Research & Development
R&D starts from a technical question your team can’t settle on paper, such as whether a model can reach the accuracy your product needs or whether your data supports the idea at all. We turn that question into a short, measured experiment and report what the evidence shows, so your next decision rests on results you can inspect and rerun.
You need this if…
- A key feature depends on a technique nobody on your team has shipped before.
- Your roadmap assumes a performance or accuracy level that no one has actually measured yet.
- You need to choose between two technical approaches and the debate keeps going in circles.
- Investors or your board want evidence that the core technology works before funding the full build.
The problem
Some technical questions need testing before a detailed plan is possible. Building a full system around an unproven approach is expensive; a prototype helps assess feasibility before committing.
Who it's for
Innovation teams and founders investigating technical questions that could change their development plans.
What you get
A focused prototype and evidence on technical feasibility, sufficient to decide whether to proceed, change course, or stop without committing the roadmap to a guess.
What we deliver
Experiment plan
A short document stating the question, the hypothesis, the success and stop criteria, and the time box. Everyone agrees on what counts as a result before any code is written.
Working proof of concept
Code that answers the question on your real data or in conditions close to production. It is built to learn quickly, and we say clearly which parts could be reused.
Measured results
Benchmarks, error analysis and observed limits, with the scripts and datasets needed to reproduce them. You see where the approach holds up and the conditions under which it breaks down.
Decision memo
A short written recommendation on whether to go further, with the reasoning behind it, the risks still open and what a next phase would need to prove.
How we work
Frame the question
We reduce the idea to one testable question, write down which result would mean go or stop, and agree with you on a fixed time box.
Review what exists
We check published work, open-source tools and off-the-shelf services first. Sometimes the answer already exists, and the experiment can start from a stronger baseline.
Build and measure
We build the smallest setup that can answer the question, run it against your data and the agreed metrics, and log every trial, including the failed ones.
Decide together
When the time box ends, we review the results against the criteria set at the start, then decide to continue, change course or stop.
Frequently asked questions
What’s the difference between a proof of concept, a prototype and an MVP?
A proof of concept answers whether something is technically possible. A prototype shows how it could look and behave for users. An MVP is real software put in front of real customers to test demand. Our R&D work mostly sits at the proof-of-concept stage, sometimes extended into a technical prototype when the question calls for it.
How long does an R&D phase take, and what if the results are negative?
It depends on the question, access to data and how much infrastructure has to be set up. A focused feasibility study often fits in a few weeks. A negative result is still useful: it tells you early, with evidence, that an approach won’t hold, before the roadmap and budget are committed to it.
Can this work qualify for the CIR or CII tax credits?
In France, some R&D or innovation work may be eligible for the research tax credit (CIR) or the innovation tax credit (CII), depending on the technical uncertainty it addresses. Eligibility should be confirmed with your accountant, as the rules have changed recently. On our side, we keep a written record of hypotheses, experiments and results as the work happens.
Our other services
Custom Software Development
Production software with tested code, documented architecture, monitoring, and a handover your team can use.
Artificial Intelligence & Machine Learning
From feature stores to deployed models and evaluation. ML systems designed for operation in production.
Technical Architecture
Architectural decisions shape data flows, service boundaries, and the platform’s ability to evolve.
Automation & Integration
Connect existing systems and automate the manual transfers between them.
Technical Consulting
Technical advice on architecture, hiring, and technical due diligence decisions with lasting implications.
Not sure which service applies?
Many projects involve more than one service. Describe the problem, and we’ll outline a technical approach.